Method and apparatus for automated detection of masses in digital images

ABSTRACT

A method and apparatus for the automated detection of masses in a digital mammogram, the method for use in a computer aided diagnosis system for assisting a radiologist in identifying and recognizing suspicious portions of the digital mammogram. A gradient image is created from the digital mammogram, and information in the gradient image is processed for identifying masses. In a preferred embodiment, a portion of a spiculation detection algorithm is applied to the gradient image for identifying masses. The spiculation detection algorithm comprises a line detection portion and a post-line detection portion, and it is the post-line detection portion which is applied to the gradient image for identifying masses. Advantageously, computer programs which have already been written for spiculation detection may, with minor modifications, be ported into mass detection programs.

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] The subject matter of this application is related to the subjectmatter of U.S. patent application Ser. No. 08/676,660, entitled “Methodand Apparatus for Fast Detection of Spiculated Lesions in DigitalMammograms,” filed on Jul. 10, 1996 and assigned to the assignee of thepresent invention. The above application is hereby incorporated byreference into the present application.

FIELD OF THE INVENTION

[0002] The present invention relates to the field of computer aideddiagnosis of abnormal lesions in medical images. In particular, theinvention relates to a fast algorithm for detecting masses in a digitalmammogram to assist in the detection of malignant breast cancer tumorsat an early stage in their development.

BACKGROUND OF THE INVENTION

[0003] Breast cancer in women is a serious health problem, the AmericanCancer Society currently estimating that over 180,000 U.S. women arediagnosed with breast cancer each year. Breast cancer is the secondmajor cause of cancer death among women, the American Cancer Societyalso estimating that breast cancer causes the death of over 44,000 U.S.women each year. While at present there is no means for preventingbreast cancer, early detection of the disease prolongs life expectancyand decreases the likelihood of the need for a total mastectomy.Mammography using x-rays is currently the most common method ofdetecting and analyzing breast lesions.

[0004] The detection of suspicious portions of mammograms is animportant first step in the early diagnosis and treatment of breastcancer. FIG. 1A shows a continuum of potentially cancerous shapes foundin mammograms, ranging from sharply defined masses on the left, movingrightward to somewhat spiculated (i.e., stellar-shaped) masses, mostlyspiculated masses, highly spiculated masses, and then finally to purespiculations on the right.

[0005] Sharply defined masses such as those at the left of FIG. 1A arerarely associated with malignant tumors, while the presence ofspiculated masses is a strong indicator of malignancy. Purespiculations, however, are often found among normal fibrous breasttissue and may not indicate a cancerous condition at all. Overall, boththe mass qualities and “spiculatedness” qualities of shapes found inmammograms must be analyzed in locating suspicious portions of themammogram.

[0006] While it is important to detect the suspicious portions of anx-ray mammogram as early as possible, i.e. when they are as small aspossible, practical considerations can make this difficult. Inparticular, a typical mammogram may contain myriads of linescorresponding to fibrous breast tissue, and the trained, focused eye ofa radiologist is needed to detect suspicious features among these lines.Moreover, a typical radiologist may be required to examine hundreds ofmammograms per day, leading to the possibility of a missed diagnosis dueto human error.

[0007] Accordingly, the need has arisen for a computer-assisteddiagnosis (CAD) system for assisting in the detection of abnormallesions in medical images. The desired CAD system digitizes x-raymammograms to produce a digital mammogram, and performs numerical imageprocessing algorithms on the digital mammogram. The output of the CADsystem is a highlighted display which directs the attention of theradiologist to suspicious portions of the x-ray mammogram. The desiredcharacteristics of a CAD system are high speed (requiring lessprocessing time), high sensitivity (the ability to detect subtlesuspicious portions), and high specificity (the ability to avoid falsepositives).

[0008] Many algorithms for processing digital mammograms in CAD systemsstart by processing the digital mammogram to locate masses (or“densities”). After this step, the “spiculatedness” of these masses ischaracterized. See Yin et. al., “Computerized Detection of Masses inDigital Mammograms: Analysis of Bilateral Subtraction Images,” Med.Phys. 18(5), September/October 1991, pp. 955-963, and Sahiner et. al.,“Classification of Masses on Mammograms Using a Rubber-BandStraightening Transform and Feature Analysis,” Medical Imaging 1996,SPIE Symposium on Medical Imaging (San Diego, Calif.), Paper No. 2710-06at p. 204, the contents of which are hereby incorporated by referenceinto the present application.

[0009] A key shortcoming of the above serial approach, in which massesare first detected and then analyzed in a subsequent step, is that somevery suspicious shapes are not recognized. In particular, those masseswhich are small, but which are highly spiculated, often do not survivethe “first cut” of the mass detection routine, which will not recognizemasses having density characteristics below a certain threshold. Thisshortcoming was recognized by Nico Karssemeijer in “Recognition ofStellate Lesions in Digital Mammograms,” Digital Mammography:Proceedings of the 2nd International Workshop on Digital Mammography,York, England, 10-12 July 1994 (Elsevier Science 1994), pp. 211-219, thecontents of which are hereby incorporated by reference into the presentapplication. There, Karssemeijer proposes an algorithm for the directdetection of spiculations (“stellate patterns”) in a digital mammogramwithout assuming the presence of a central mass.

[0010] Another method for the direct detection of spiculations indigital mammograms is provided in Kegelmeyer et. al., “Computer-aidedMammographic Screening for Spiculated Lesions,” Radiology 191:331-337(1994), the contents of which are hereby incorporated by reference intothe present application. Yet another method for the direct detection ofspiculations, along with linear classification steps which use both massand spiculation information in identifying suspicious portions of thedigital mammogram, is provided by Roehrig et. al. in the abovereferenced U.S. patent application entitled “Method and Apparatus forFast Detection of Spiculated Lesions in Digital Mammograms.”

[0011] One improvement which may be incorporated into CAD systems isfurther integration and symmetry between of the steps of mass detectionand spiculation detection. Such integration and symmetry would providefor more efficient programming of the CAD system, more efficientprocessing by the CAD system, and reduced memory requirements. Inparticular, it would be desirable to execute both mass detection andspiculation detection steps using the same or similar computationengines in the CAD system. Additionally, it would be desirable toharness algorithmic advances made in spiculation detection algorithms byapplying them to mass detection algorithms.

[0012] Accordingly, it is an object of the present invention to providea fast computer-assisted diagnosis (CAD) system for assisting in theidentification of suspicious masses and spiculations in digitalmammograms, the CAD system being capable of producing an output whichdirects attention to suspicious portions of the x-ray mammogram forincreasing the speed and accuracy of x-ray mammogram analysis.

[0013] It is a further object of the present invention to provide amethod for adapting a spiculation detection algorithm for use in a massdetection algorithm, for increased symmetry and integration of CADsystem algorithms, and for adapting algorithmic advances in spiculationdetection algorithms to mass detection algorithms.

SUMMARY OF THE INVENTION

[0014] These and other objects of the present invention are provided forby an improved CAD system capable of detecting masses in a digitalmammogram image, wherein a gradient image is created from the digitalmammogram, and wherein information in the gradient image is thenprocessed for identifying masses. In a preferred embodiment, a portionof a spiculation detection algorithm is applied to the gradient imagefor identifying masses.

[0015] A spiculation detection algorithm normally comprises a linedetection portion and a post-line detection portion. However, in apreferred embodiment, the post-line detection portion of the spiculationdetection algorithm is applied to a gradient image for identifyingmasses, instead of being applied to a line image for identifyingspiculations. Thus, instead of being provided with line and directionparameters, the post-line detection portion of the spiculation detectionalgorithm is provided with gradient magnitude and gradient directionparameters. The post-line detection portion of the spiculation detectionalgorithm then operates normally, except that its output corresponds tomass location and mass density information instead of spiculationlocation and spiculation intensity information.

[0016] Advantageously, computer programs which have already been writtenfor spiculation detection may, with minor modifications, be ported intomass detection programs. Furthermore, advances in the speed and accuracyof spiculation detection algorithms may be applied for use in creatingfaster and more accurate mass detection algorithms.

[0017] When a post-line detection portion of a spiculation detectionalgorithm has been adapted according to a preferred embodiment, theresulting method of detecting masses operates as follows. A gradientplane is computed from the digital mammogram, each pixel of the gradientplane having gradient magnitude and gradient direction information. Aset of edge pixels S in the gradient plane is selected by selectingthose pixels having a gradient magnitude greater than a first threshold.A set of candidate pixels in the digital mammogram image is thenselected, and, for each candidate pixel “icand”, a first density metricG1 _(icand) is computed. The metric G1 _(icand), termed a densitymagnitude metric, is computed according to the steps of (a) selecting aneighborhood of pixels NH_(icand) around the candidate pixel, (b)selecting a small region R_(icand) around the candidate pixel, (c)selecting a first set of pixels in the neighborhood NH_(icand) havinggradient directions pointing toward the small region R_(icand) and beingmembers of the set S having a gradient magnitude greater than apredetermined lower threshold, and (d) counting the number of pixels inthe first set, wherein the first density metric G1 _(icand) isproportional to the number of pixels in the first set.

[0018] A second density metric G2 _(icand), termed a mass isotropymetric, is also computed for each candidate pixel icand, according tothe steps of (a) selecting K spatial bins (icand,k) extending radiallyfrom the candidate pixel and being arranged in a radially symmetricmanner around the candidate pixel, (b) for each pixel (icand, jpoint) ofthe first set of pixels, identifying the spatial bin (icand, k) in whichthe pixel (icand,jpoint) is located, (c) computing a number of pixelsn_(icand,k) in each spatial bin (icand,k), and (d) analyzing thestatistical distribution of the number n_(icand,k) as k is varied,wherein the mass isotropy metric G2 _(icand) is proportional to thenumber of values k for which n_(i,k) is greater than a median value forrandom gradient orientations. Finally, the density magnitude and massisotropy metrics G1 and G2 are evaluated according to a linearclassifier or neural network method for determining the locations andintensities of suspicious masses in the digital mammogram.

BRIEF DESCRIPTION OF THE DRAWINGS

[0019]FIG. 1A shows a continuum of potentially cancerous shapes found indigital mammograms, including shapes which may be detected by a computeraided diagnostic (CAD) system in accordance with a preferred embodiment;

[0020]FIG. 1B shows an outside view of a CAD system according to apreferred embodiment;

[0021]FIG. 1C shows a block diagram of a CAD processing unit of a CADsystem according to a preferred embodiment;

[0022]FIG. 2 is a flowchart representing overall steps taken by the CADsystem of FIG. 1B;

[0023]FIG. 3 is a flowchart representing overall steps normally taken ina spiculation detection algorithm;

[0024]FIG. 4 is a flowchart showing a line detection step of aspiculation detection algorithm;

[0025]FIG. 5 is a flowchart representing steps taken by the a post-linedetection step of a spiculation detection algorithm;

[0026]FIG. 6 is a diagram of a neighborhood pixel in relation to acandidate pixel showing parameters used in a post-line detection step ofa spiculation detection algorithm;

[0027]FIG. 7 is a flowchart representing overall steps taken in a massdetection algorithm according to a preferred embodiment;

[0028]FIG. 8 is a flowchart representing steps taken by a mass detectionalgorithm as applied to a gradient image in accordance with a preferredembodiment;

[0029]FIG. 9 is a diagram of a neighborhood pixel in relation to acandidate pixel showing parameters used in a mass detection algorithm inaccordance with a preferred embodiment.

[0030]FIG. 10 is a flowchart representing overall steps taken by the CADsystem of FIG. 1B in accordance with another preferred embodiment.

DETAILED DESCRIPTION OF THE INVENTION

[0031]FIG. 1B shows an outside view of a computer aided diagnostic (CAD)system 100 for assisting in the identification of spiculated lesions inmammograms according to the present invention. CAD system 100 is used asa step in the processing of films for mammography exams. CAD system 100comprises a CAD processing unit 102 and a viewing station 104. Ingeneral, CAD processing unit 102 scans an x-ray mammogram into a digitalmammogram image, processes the image, and outputs a highlighted digitalmammogram for viewing at viewing station 104.

[0032]FIG. 1C shows a block diagram of CAD processing unit 102. CADprocessing unit 102 comprises a digitizer 103, such as a laser scannerwith 50 micron resolution, for digitizing a developed x-ray mammogram101, the x-ray mammogram 101 being shown in FIG. 1B at an input to theCAD processing unit 102. CAD processing unit 102 generally includeselements necessary for performing image processing including parallelprocessing steps. In particular, CAD processing unit 102 includeselements such as a central control unit 105, a memory 108, a parallelprocessing unit 110, and I/O unit 112. It is to be appreciated that inaddition to the mass and spiculation detection algorithms disclosedherein, processing unit 102 is capable of performing a multiplicity ofother image processing algorithms, such as linear classifier algorithmsand neural network algorithms, either serially or in parallel with thedisclosed mass and spiculation detection algorithms.

[0033] Viewing station 104 is for conveniently viewing both the x-raymammogram 101 and the output of the CAD processing unit 102 on a displaydevice 118. The display device 118 may be, for example, a CRT screen.The display device 118 typically shows a highlighted digital mammogramcorresponding to the x-ray mammogram 101, the highlighted digitalmammogram having information directing the attention of the radiologistto special areas which may contain spiculations as determined by imageprocessing steps performed by the CAD processing unit 102. In oneembodiment of the invention, the highlighted digital mammogram will haveblack or red circles superimposed around those locations correspondingto spiculated lesions.

[0034] Viewing station 104 also comprises a backlighting station 120 forviewing the actual x-ray mammogram 101 itself. The radiologist isassisted by the CAD system 100 by viewing the display device 118, whichthen directs the attention of the radiologist to the spiculated portionsof the actual x-ray mammogram 101 itself. It is to be appreciated thatthe CAD processing unit 102 is capable of performing other imageprocessing algorithms on the digital mammogram in addition to or inparallel with the algorithms for detecting masses and spiculations inaccordance with the present invention. In this manner, the radiologistmay be informed of several suspicious areas of the mammogram at once byviewing the display device 118, spiculations being one special type ofthe suspicious area.

[0035] After x-ray mammogram 101 has been developed, it is inserted intothe CAD system 100, which will ideally be located near the x-raydevelopment area of a mammography clinic. After being digitized bydigitizer 103, the x-ray mammogram will be transported using means notshown to the viewing station 104 for viewing by the radiologist alongwith the output of the display device 118 as described above. After thex-ray mammogram 101 has passed through the CAD system 100, it will betaken away and will undergo the same processing currently practiced inclinics. It is to be noted that memory 108 of CAD processing unit 102may be used in conjunction with I/O unit 112 to generate a permanentrecord of the highlighted digital mammogram described above, and/or mayalso be used to allow non-real-time viewing of the highlighted digitalmammogram.

[0036]FIG. 2 shows the general steps performed by CAD processing unit102 on the x-ray mammogram. At step 202, the x-ray mammogram is scannedin and digitized into a digital mammogram. The digital mammogram may be,for example, a 3000×4000 array of 12-bit gray scale pixel values. Such adigital mammogram would generally correspond to a typical 8″×10″ x-raymammogram which has been digitized at a 50 micron spatial resolution.Because a full resolution image such as the 3000×4000 image describedabove is not necessary for the effectiveness of the preferredembodiment, the image may be locally averaged, using steps known in theart, down to a smaller size corresponding, for example, to a 200 micronspatial resolution. At such a resolution, a typical image would then bean M×N array of 12-bit gray scale pixel values, with M being near 900,for example, and N being near 1200, for example. In general, however,either the full resolution image or the locally averaged image may beused as the original digital mammogram in accordance with the preferredembodiment. Without limiting the scope of the present disclosure, andfor clarity of disclosure, the “digital mammogram image” is consideredto be an exemplary M×N array of 12-bit gray scale pixel values.

[0037]FIG. 2 shows the digital mammogram image being processed at step204 by mass detection algorithms and spiculation detection algorithms. Atypical mass detection algorithm receives a digital mammogram image andproduces an output plane comprising, for each pixel location (i,j), ameasure corresponding to mass characteristics of the digital mammogramimage at (i,j). Examples of such algorithms are disclosed, for example,in Yin et al., supra, and in U.S. Pat. No. 5,133,020 to Giger et al,entitled “Automated Method and System for the Detection andClassification of Abnormal Lesions and Parenchymal Distortions inDigital Medical Images,” the latter disclosure being hereby incorporatedby reference into the present application. Mass characteristics mayinclude mass area, mass elongation, mass contrast, and other measureswhich reflect mass events. Mass characteristics may also includeinformation derived from region-growing algorithms known in the art anddescribed, for example, in Gonzalez, Digital Image Processing at pp.369-375, the disclosure of which is incorporated herein by referenceinto the present application.

[0038] A typical spiculation detection algorithm receives a digitalmammogram image and produces an output plane comprising, for each pixellocation (i,j), a measure corresponding to spiculation characteristicsof the digital mammogram image at (x,y). Examples of spiculationcharacteristics are provided in U.S. patent application Ser. No.08/676,660, supra, and may include, for example, a cumulative arrayC(i,j) which is related to the presence of spiculations centered at(i,j), and an eccentricity plane ECC(i,j) which is inversely related tocircularity of spiculations centered at (i,j).

[0039]FIG. 2 further shows a step 206, which uses the masscharacteristics and spiculation characteristics generated at step 204for identifying and prioritizing suspicious portions of the digitalmammogram by using linear classifiers or neural networks. In general,each location (i,j) is evaluated separately by consideration of thevarious mass and spiculation characteristics at that pixel location.

[0040] At step 206, a method of linear classifiers using rule-based cuts(thresholds) on each feature or combinations of features may be used todetermine suspicious regions. By way of non-limiting example, the valueof the cumulative array C(i,j) may simply be thresholded by a thresholdvalue. As another example, a plot may be made of (1/ECC(i,j)) versusmass elongation for pixels (i,j) having a mass area value above acertain mass area threshold. Minimum threshold values along the abscissaand ordinate may be selected, and events falling in the upper rightquadrant may be selected as suspicious regions, with a view toward notidentifying large elongated masses unless they are associated with ahighly circular spiculation. The values of thresholds used may bedetermined empirically by examining the distribution of true and falsepositive indications.

[0041] As a further nonlimiting example, at step 206 a simple linearclassifier may be constructed to indicate a suspicious location for any(i,j) for which all the following events occur: (a) the cumulative arrayC(i,j) is greater than a first cumulative array threshold, indicating alarge spiculation; (b) the mass area around the pixel (i,j) is greaterthan a first mass area threshold indicating a large mass; and (c) theeccentricity value ECC(i,j) is below a first spiculation eccentricitythreshold, indicating the presence of circular spiculated shape.Following step 206, the digital mammogram image and list of suspiciouslocations and information is sent for display to the viewing station 104at step 208.

[0042]FIG. 3 shows in more detail the steps associated with aspiculation detection algorithm for use at step 204. The spiculationdetection algorithm at FIG. 3 is similar to that described inKarssemeijer, “Recognition of Stellate Lesions in Digital Mammograms,”supra. At step 302, a line image is computed from the digital mammogramimage, each line image pixel having a line magnitude LMAG(i,j) and linedirection LARG(i,j). Generally, LMAG(i,j) is 1 if the pixel (i,j) isassociated with a line, and LMAG is 0 otherwise.

[0043]FIG. 4 shows steps corresponding to the line image generation step302 of FIG. 3. Shown at FIG. 4 is the direction detection step 402 fordetecting at each pixel (i,j) a direction corresponding to a line, ifany, passing through the pixel (i,j) in the digital mammogram image.Direction detection step 402 comprises the step of separately convolvingthe digital mammogram image with three Gabor kernels K₀, K₆₀, and K₁₂₀.The Gabor kernels are derived from the Gabor filter which, as known inthe art, is the second derivative of a Gaussian kernel given by:$\begin{matrix}{{G\left( {r,\sigma} \right)} = {\frac{1}{2\quad \pi \quad \sigma^{2}}\exp \quad \left( {\left( {- r^{2}} \right)/\left( {2\sigma^{2}} \right)} \right)}} & (1)\end{matrix}$

[0044] The second derivative of this function with respect to x,quantized into a finite sized integer array, yields the K₀ kernel. It isto be appreciated that the K₀ kernel is a two dimensional convolutionkernel which is generally small (e.g., 11×11 pixels) in comparison tothe digital mammogram image (e.g., 900×1200 pixels). By rotating the K₀array by 60 degrees and 120 degrees, two other kernels K₆₀ and K₁₂₀ areobtained. The step of separately convolving the digital mammogram withthe kernels K₀, K₆₀, and K₁₂₀ yields three images W₀(i,j), W₆₀(i,j), andW₁₂₀(i,j), respectively.

[0045] At step 402, direction information LARG(i,j) for each pixel (i,j)is obtained by using a formula which can be derived from relationsdisclosed in Koenderink and Van Doorn, “Generic Neighborhood Operators,”IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 14,No. 6 (June 1992) and given by: $\begin{matrix}{{G\left( {i,j} \right)} = {\frac{1}{2}a\quad \tan \sqrt{3}\left( \frac{{W_{60}\left( {i,j} \right)} - {W_{120}\left( {i,j} \right)}}{{W_{60}\left( {i,j} \right)} + {W_{120}\left( {i,j} \right)} - {2{W_{0}\left( {i,j} \right.}}} \right.}} & (2)\end{matrix}$

[0046]FIG. 4 further shows line detection step 404 for detecting lineinformation in the digital mammogram image. Positive contrast (light)lines are important, as opposed to negative contrast (dark) lines, sincethe former is how spiculations are manifested in x-ray films. Linedetection step 404 comprises the step of deriving a function W_(σ)(i,j)from the images W₀(i,j), W₆₀(i,j), and W₁₂₀(i,j) using a formuladisclosed in the Koenderink reference cited supra: $\begin{matrix}{= {\frac{1}{3}\left( {1 + {2\quad \cos \quad \left( {2\quad {{LARG}\left( {i,j} \right)}} \right){W_{0}\left( {i,j} \right)}}\quad + {\frac{1}{3}\left( {1 - {\cos \quad \left( {2\quad {{LARG}\left( {i,j} \right)}} \right){W_{60}\left( {i,j} \right)}}\quad + {\frac{1}{3}\left( {1 - {\cos \quad \left( {{2\quad {{LARG}\left( {i,j} \right)}} - {\sqrt{3}\sin \quad \left( {2\quad {{LARG}\left( {i,j} \right)}} \right)W_{1}}} \right.}} \right.}} \right.}} \right.}} & (3)\end{matrix}$

[0047] After being computed, W_(σ)(i,j) is thresholded at a constantpositive threshold value for obtaining a binary line image: for eachpixel (i,j), if W_(σ)(i,j) is greater than that threshold value,LMAG(i,j) is set to 1; otherwise, LMAG(i,j) is set to 0.

[0048] Thus, after step 404, there exists a line image comprising linemagnitude information LMAG(i,j) and line direction information LARG(i,j)corresponding to the digital mammogram image for further processing bysubsequent spiculation detection steps. While there are several methodsknown in the art for line image generation, the above approach isemployed because the computationally intensive parts consist of thethree convolutions performed to obtain W₀(i,j), W₆₀(i,j), and W₁₂₀(i,j),and these convolutions are 5easily implemented in a highly parallelprocessor such as that used in processing unit 104. By implementingthese convolutions in the spatial domain in a hardware parallelprocessor, the speed of computation easily meets normal through-putrequirements for clinical practice.

[0049] Referring again to FIG. 3, at step 304 a set of line pixels S inthe digital mammogram image is identified. The set of line pixels S issimply the set of pixels having coordinates (i,j) for which LMAG(i,j) isequal to 1. At step 305 a set of candidate pixels is identified, thecandidate pixels being those locations in the digital mammogram whichmay correspond to the centers of spiculations. While the center of aspiculation may fall within the set S of line pixels identified above,this does not always occur. In particular, a spiculation may be a set oflines which radiate from a common center but which do not actuallyoccupy the center pixel itself. Accordingly, the candidate pixels may beselected from an area encompassing the entire breast tissue area of thedigital mammogram.

[0050] More particularly, the selection of the candidate pixels may beperformed by (1) identifying the breast tissue area of the digitalmammogram and then (2) selecting pixels within that area as candidatepixels. The step of identifying the breast tissue area may be performedby a simple thresholding of the entire digital mammogram image at a lowthreshold value. This operation will have the effect of cancelling outall background (non-breast) regions of the digital mammogram.

[0051] The portion of the digital mammogram which survives thethresholding operation, i.e. the breast tissue area, is then sampled toprovide the set of candidate pixels. In a preferred embodiment, thebreast tissue area is sampled on a regular grid, e.g., a rectangulargrid, at a regular sampling intervals such as every m^(th) pixel. Whilea typical value for the sampling interval m may be 4 for a 900×1200digital mammogram image, the scope of the present disclosure is not solimited, and the breast tissue may be sampled at greater or lesserintervals as appropriate, including an interval of m=1.

[0052] At step 306, two “spiculatedness” or “stellateness” metrics arecomputed for each candidate pixel. For clarity of disclosure, thecandidate pixels will be referenced by a linear index “icand”, it beingunderstood that each candidate pixel actually has a coordinate(i_(icand),j_(icand)) in the image. In particular, a stellatenessmagnitude metric F1 _(icand) and an isotropy metric F2 _(icand) arecomputed, as will be described further infra. At step 308, thestellateness magnitude F1 and isotropy metric F2 are set to zero for allnon-candidate pixels. All pixels in the line image having then beenassigned values for F1 and F2, the stellateness magnitude metric F1(i,j)and isotropy metric F2(i,j) are then provided to the classification step206 of FIG. 2 for determination of suspicious portions of the digitalmammogram, using methods generally known in the art.

[0053]FIG. 5 shows a block diagram outlining step 306 for computing thestellateness magnitude metric F1 _(icand) and isotropy metric F2_(icand) for each candidate pixel. At step 502, a neighborhood of pixelsNH_(icand) around the icand^(th) candidate pixel is selected. Althoughthe scope of the preferred embodiment is not so limited, theneighborhood NH_(icand) is generally chosen as an annulus around theicand^(th) candidate pixel, the annulus having an inner radius r_(min)and an outer radius r_(max). By way of example and not by way oflimitation, typical values for r_(min) and r_(max) may be 4 mm and 16mm, respectively.

[0054]FIG. 6 shows a conceptual diagram of the icand^(th) candidatepixel and its surrounding neighborhood NH_(icand). At step 504, a smalltarget region R_(icand), having a radius of the same designation, isidentified around the candidate pixel. The target region R_(icand) isalso shown in FIG. 6. By way of example and not by way of limitation, atypical value for R_(icand) may be 2 mm. At step 506, a subset of pixelslying in the neighborhood NH_(icand) is identified, this subset havingthe property that (a) each pixel is also in the set S of pixels havingLMAG(i,j) equal to 1, and (b) the line directions LARG(i,j) for eachpixel points toward the target region R_(icand).

[0055] Generally, those candidate pixels having a larger number ofsurrounding pixels with line directions pointing toward the candidatepixel icand are more likely to be at the center of spiculations.Accordingly, a stellateness magnitude measure would be proportional tothe number of pixels surrounding the icand^(th) pixel having suchcharacteristics. Denoting the length of a line between the icand^(th)pixel and the pixel jpoint as r_(icand,jpoint), and denoting the angleformed by this line as Ø_(icand,jpoint), the number n_(icand) iscomputed in step 506 as expressed in the following equations.$\begin{matrix}{\sum\limits_{{{nt}\quad ɛ\quad {NH}_{{ic}\quad {and}}}\bigcap\quad S}{h\left( {{{LARG}\left( {i_{j\quad {point}},j_{j\quad {point}}} \right)},\phi_{{{ic}\quad {and}},{j\quad {point}}},r} \right.}} & (4) \\{{{{if}\quad {abs}\quad \left( {\phi_{{{ic}\quad {and}},{j\quad {point}}} - {{LARG}\quad \left( {i_{j\quad {point}},j_{j\quad {point}}} \right)}} \right)} < \frac{R_{ic}}{r_{{ic}\quad {and}}}}{{{else}\quad h} = 0}} & (5)\end{matrix}$

[0056] For purposes of better understanding equation (5) in relation toFIG. 6, it is to be appreciated that the tangent of a small angle isapproximately equal to the value of that small angle in radians.Accordingly, the argument of the absolute value symbol in equation (5)is an approximation of the tangent of an angle formed by (a) a vectorpointing from icand to jpoint in FIG. 6, and (b) a vector originatingfrom jpoint and pointing in the direction ofLARG(i_(jpoint),j_(jpoint)). thus, if LARG(i_(jpoint),j_(jpoint)) pointsdirectly at the point icand or a nearby point, the value of this angleis zero or nearly zero, respectively.

[0057] For more optimal use in subsequent classifier steps, thestellateness magnitude measure F1 _(icand) is based on a normalizedversion of n_(icand). In order to normalize n_(icand), its mean valueand variance are estimated under the assumption that the line directionorientation map is a uniformly distributed random noise pattern. A meanprobability p that a pixel in this random map points to the targetregion R_(icand) is shown in the following equation. $\begin{matrix}{p = {\frac{2}{\pi \quad N_{{ic}\quad {and}}}{\sum\limits_{{j\quad {point}\quad ɛ\quad {NH}_{{ic}\quad {and}}}\bigcap\quad S}\left( \frac{R_{\quad}}{r_{{icand},{j\quad {point}}}} \right)}}} & (6)\end{matrix}$

[0058] In Eq. (6), N_(icand) is the total number of pixels in theneighborhood NH_(icand). The stellateness magnitude metric F1 _(icand)is then computed at step 508 according to the following equation.$\begin{matrix}{{F1}_{{ic}\quad {and}} = \frac{n_{{ic}\quad {and}} - {pN}_{{ic}\quad {and}}}{\sqrt{N_{{ic}\quad {and}}{p\left( {1 - p} \right)}}}} & (7)\end{matrix}$

[0059] Because of this normalization, the sensitivity of thestellateness magnitude metric F1 _(icand) and its range do not changesystematically when the neighborhood or target size R_(icand) arechanged. This enables changing these parameters adaptively and avoidsproblems at the breast edge.

[0060] If an increase in the number of pixels oriented toward the centeris found in only a few directions, that is, if the distribution of thesepoints is less circular around the icand^(th) pixel, it is less likelythat the site being evaluated belongs to a suspicious spiculated lesion.On the other hand, if this distribution is more isotropic around theicand^(th) pixel, the level of suspicion should increase. Accordingly, asecond measure termed the isotropy metric F2 _(icand) is constructed.

[0061] To construct F2 _(icand), K radial direction bins are formedwithin the neighborhood NH_(icand), and are placed around the icand^(th)pixel in a radially symmetric fashion, as shown in FIG. 6. By way ofexample and not by way of limitation, a typical value for the number ofbins K is 16. At step 510, each pixel identified at step 506, that is,each pixel in NH_(icand) which are in the set S havingLMAG(i_(jpoint),j_(jpoint))=1 and which point toward the regionR_(icand), is placed into the appropriate k^(th) direction bin, wherek=1,2, . . . , K. At step 512, the number of pixels n_(icand,k) in eachbin are computed.

[0062] At step 514, a number n⁺ is computed as follows. In each radialdirection bin k, the mean probability of finding n_(icand,k) pixelsoriented toward R_(icand) is calculated by applying Eq. (7) to each binseparately. Using binomial statistics, the number n⁺ is computed as thenumber of times that n_(icand,k) is larger than the median valuecalculated for random orientations as k varies from 1 to K.

[0063] Finally, at step 516, the isotropy measure F2 _(icand) is definedby the following equation. $\begin{matrix}{{F\quad 2_{{ic}\quad {and}}} = \frac{n + {{- K^{\prime}}/2}}{\sqrt{K^{\prime}/4}}} & (8)\end{matrix}$

[0064] In equation (9), K′/2 is the expected value of n⁺ when no signalis present. To avoid boundary effects, only bins with a minimum numberof contributing sites is considered. Therefore, near the breast edge theactual number of bins K that are formed is to be reduced. The standarddeviation of random fluctuations in the denominator of Eq. (9)normalizes the expression.

[0065] Once the values F1 _(icand) and F2 _(icand) are computed for eachcandidate pixel icand, the step 308 may be carried out, at which allvalues F1 and F2 for non-candidate pixels are set to zero. At thispoint, there is sufficient information to form two spiculation metricplanes F1(i,j) and F2(i,j) for processing in linear classifier/neuralnetwork step 206 of FIG. 2. Importantly, at step 206 the stellatenessmeasure F1(i,j) and isotropy measure F2(i,j), which increase as thelikelihood of a suspicious spiculation increases, may be used inconjunction with other mass and spiculation metrics in making a finaldetermination of the suspicious locations in the digital mammogramimage.

[0066] Generally, the spiculation detection algorithm outlined at FIG. 3can be broken down into two overall steps: a line detection stepcomprising step 302, and a post-line detection step comprising steps304, 306, and 308. It has been found that the spiculation detectionalgorithm at FIG. 3 and other spiculation detection algorithms may beadapted for operation as mass detection algorithms. In particular,whenever the spiculation detection algorithm can be broken down into aline detection step and a post-line detection, it is capable ofadaptation into a mass detection algorithm by first computing a gradientimage and than applying the post-line detection step of the spiculationdetection algorithm to the gradient image instead of the line image.

[0067]FIG. 7 shows the steps taken by a mass detection algorithm inaccordance with a preferred embodiment. It is to be appreciated thatthis algorithm is similar to that disclosed in Brake and Karssemeijer,“Detection of Stellate Breast Abnormalities,” Digital Mammography '96:Proceedings of the 3rd Int'l Workshop on Digital Mammography, Chicago,USA (Jun. 9-12 1996), pp. 341-46, the contents of which are herebyincorporated by reference into the present application.

[0068] At step 702, the gradient image GMAG(i,j) and GARG(i,j) arecomputed from the digital mammogram image. The gradient orientations arecomputed on a larger spatial scale than the line direction measures atstep 302, which is more appropriate for masses. Pixels that are inside amass will be surrounded by pixels whose gradient directions point awayfrom the central pixel; where 180 degrees is added to each gradientdirections GARG(i,j), these pixels point toward the central pixel.However, if no structure is present, a more or less random direction isfound.

[0069] At step 702, the digital mammogram image is convolved with twofirst derivatives of a Gaussian to produce I_(x) and I_(y), thegradients in the x and y directions. This space-scale approach gives arotation invariant gradient estimation that can be computed easily on anumber of scales. A Gaussian (see Eq. (1)) with a predetermined scale(σ=3 mm) is suitable for this purpose. In a preferred embodiment, it hasbeen found that smaller scales (smaller values of σ) are better fordetection of smaller masses, while larger scales (larger values of σ)are better for detection of larger masses. For example, as will bediscussed infra, two separate passes using a first value of σ=3 mm and asecond value of σ=0.2 mm has been found to be useful.

[0070] The gradient direction and magnitude are found according to thefollowing equations. $\begin{matrix}{{{GARG}\quad \left( {i,j} \right)} = {\tan^{- 1}\left( \frac{I_{y}}{I_{x}} \right)}} & (9)\end{matrix}$

 GMAG(i,j)={square root}{square root over (I _(y) I _(y) +I _(x) I_(x))}  (10)

[0071]FIG. 7 then shows steps 704-708 which are highly analogous to thesteps 304-308 in accordance with a preferred embodiment. The primarydifference is that instead of the values LMAG(i,j) and LARG(i,j) whichare supplied to step 304, the values GMAG(i,j) and GARG(i,j) aresupplied to step 704. The mass metrics computed, G1 _(icand) and G2_(icand), are analogous to the measures F1 _(icand) and F2 _(icand), inthat they are computed in an almost identical fashion, except for thesubstitution of arguments discussed above. However, G1 _(icand) istermed a density magnitude measure, while G2 _(icand) is termed adensity isotropy measure in accordance with a preferred embodiment, asthese measures now correspond to mass characteristics.

[0072] A further difference is to be appreciated between the value ofGARG(i,j) computed at step 702 in relation to LARG(i,j) computed in step302. In particular, the value of line orientation LARG(i,j) is limitedto the range [0,Π] as computed at step 302, whereas the gradientorientation GARG(i,j) lies in the interval [0,2Π]. Thus, whereas Eq. (6)contains a scaling factor of 2 before the summation sign, the equivalentequation (Eq. (13) infra) will contain a scaling factor of 1.

[0073] As shown in FIG. 7, at step 704 a set of edge pixels S in thegradient image is chosen. More specifically, those points lying alongedges will correspond to the set of gradient image pixels havingGMAG(i,j) greater than a predetermined lower threshold. These pixels areselected as the set S of edge pixels.

[0074] At step 705, a set of candidate pixels is selected in a manneranalogous to the manner of step 305. In particular,

[0075] the selection of the candidate pixels may be performed by (1)identifying the breast tissue area of the digital mammogram and then (2)selecting pixels within that area as candidate pixels. Likewise, at step705 the breast tissue area is to be sampled at regular samplingintervals to provide the set of candidate pixels.

[0076] At step 706, two density metrics are computed for each candidatepixel. As before, the candidate pixels will be referenced by a linearindex icand, it being understood that each candidate pixel actually hasa coordinate (i_(icand),j_(icand)) in the gradient image. In particular,a density magnitude metric G1 _(icand) and an isotropy metric G2_(icand) are computed, as will be describe further infra. At step 708,the density magnitude metrics G1 and isotropy metrics G2 are set to zerofor all non-candidate pixels. The density magnitude metric G1 _(icand)and isotropy metric G2 _(icand) generated in the algorithm of FIG. 7 arethen provided to the classification step 206 of FIG. 2 for determinationof suspicious portions of the digital mammogram, using methods generallyknown in the art.

[0077]FIG. 8 shows a block diagram outlining step 706 of FIG. 7 forcomputing the density magnitude metric G1 _(icand) and isotropy metricG2 _(icand) for each candidate pixel. At step 802, a neighborhood ofpixels NH_(icand) around the icand^(th) candidate pixel is selected.Although the scope of the preferred embodiment is not so limited, theneighborhood NH_(icand) is generally chosen as an annulus around theicand^(th) candidate pixel, the annulus having an inner radius r_(min)and an outer radius r_(max).

[0078]FIG. 9 shows a conceptual diagram of the icand^(th) candidatepixel and its surrounding neighborhood NH_(icand). At step 804, a smalltarget region R_(icand), having a radius of the same designation, isidentified around the candidate pixel. the target region R_(icand) isalso shown in FIG. 9. At step 806, a subset of pixels lying in theneighborhood NH_(icand) is selected, this subset having the propertythat (a) each pixel is in the set S of pixels having GMAG(i,j) greaterthan the predetermined lower threshold, and (b) a vector centered atthat pixel has a gradient direction GARG(i,j) pointing toward the targetregion R_(icand).

[0079] Generally, those candidate pixels icand having a larger number ofsurrounding pixels with gradient directions pointing toward thecandidate pixel icand are more likely to be at the center of largermasses. Accordingly, a density magnitude measure would be proportionalto the number of pixels surrounding the icand^(th) pixel having suchcharacteristics. Denoting the length of a line between icand and aqualifying pixel, denoted jpoint, as r_(icand,jpoint), and denoting theangle formed by this line as Ø_(icand,jpoint), this number n_(icand) iscomputed in step 806 as expressed in the following equations.$\begin{matrix}{\sum\limits_{{{nt}\quad ɛ\quad {NH}_{{ic}\quad {and}}}\bigcap\quad S}{h\left( {{{GARG}\left( {i_{j\quad {point}},j_{j\quad {point}}} \right)},\phi_{{{ic}\quad {and}},{j\quad {point}}},r} \right.}} & (11) \\{{{{if}\quad {abs}\quad \left( {\phi_{{{ic}\quad {and}},{j\quad {point}}} - {{GARG}\quad \left( {i_{j\quad {point}},j_{j\quad {point}}} \right)}} \right)} < \frac{R_{ic}}{r_{{ic}\quad {and}}}}{{{else}\quad h} = 0}} & (12)\end{matrix}$

[0080] For more optimal use in subsequent classifier steps, the densitymagnitude measure G1 _(icand) is based on a normalized version ofn_(icand). In order to normalize n_(icand), its mean value and varianceare estimated under the assumption that the line direction orientationmap is a uniformly distributed random noise pattern. A mean probabilityp that a pixel in this random map points to the target region R_(icand)is shown in the following equation. $\begin{matrix}{p = {\frac{1}{\pi \quad N_{{ic}\quad {and}}}{\sum\limits_{{j\quad {point}\quad ɛ\quad {NH}_{{ic}\quad {and}}}\bigcap\quad S}\left( \frac{R_{\quad}}{r_{{icand},{j\quad {point}}}} \right)}}} & (13)\end{matrix}$

[0081] In Eq. (13), N_(icand) is the total number of pixels in theneighborhood NH_(icand). The density magnitude metric G1 _(icand) isthen computed at step 808 according to the following equation.$\begin{matrix}{{G1}_{icand} = \frac{n_{icand} - {pN}_{icand}}{\sqrt{N_{icand}{p\left( {1 - p} \right)}}}} & (14)\end{matrix}$

[0082] Because of this normalization the sensitivity of the densitymagnitude metric G1 _(icand) and its range do not change systematicallywhen the neighborhood or target size R_(icand) are changed. This enableschanging these parameters adaptively and avoids problems at the breastedge.

[0083] If an increase in the number of pixels oriented toward the centeris found in only a few directions, that is, if the distribution of thesepoints is less circular around the icand^(th) pixel, it is less likelythat the site being evaluated belongs to a suspicious mass. On the otherhand, if this distribution is more isotropic around the icand^(th)pixel, the level of suspicion should increase. Accordingly, a secondmeasure termed the isotropy metric G2 _(icand) is constructed.

[0084] To construct G2 _(icand), K radial direction bins are formedwithin the neighborhood NH_(icand), and are placed around the icand^(th)pixel in a radially symmetric fashion, as shown in FIG. 9. At step 810,each pixel identified at step 806, that is, each pixel in NH_(icand)which have GMAG(i_(jpoint),j_(jpoint)) greater than a predeterminedlower threshold and which point toward the region R_(icand), is placedinto the appropriate k^(th) direction bin, where k=1,2, . . . , K. Atstep 812, the number of pixels n_(icand,k) in each bin are computed.

[0085] At step 814, a number n⁺ is computed as follows. In each radialdirection bin k, the mean probability of finding n_(icand,k) pixelsoriented toward R_(icand) is calculated by applying Eq. (14) to each binseparately. Using binomial statistics, the number n⁺ is computed as thenumber of times that n_(icand,k) is larger than the median valuecalculated for random orientations as k varies from 1 to K.

[0086] Finally, at step 816, the isotropy measure G2 _(icand) is definedby the following equation. $\begin{matrix}{{G2}_{icand} = \frac{n + {{- K^{\prime}}/2}}{\sqrt{K^{\prime}/4}}} & (15)\end{matrix}$

[0087] In equation (15), K′/2 is the expected value of n⁺ when no signalis present. To avoid boundary effects, only bins with a minimum numberof contributing sites is considered. Therefore, near the breast edge theactual number of bins K that are formed is to be reduced. The standarddeviation of random fluctuations in the denominator of Eq. (15)normalizes the expression.

[0088] Once the values G1 _(icand) and G2 _(icand) are computed for eachcandidate pixel icand, the step 708 may be carried out, at which allvalues G1 and G2 for non-candidate pixels are set to zero. At thispoint, there is sufficient information to form two mass metric planesG1(i,j) and G2(i,j) for processing in linear classifier/neural networkstep 206 of FIG. 2. Importantly, at step 206 the density magnitudemeasure G1(i,j) and isotropy measure G2(i,j), which increase as thelikelihood of a suspicious spiculation increases, may be used inconjunction with other mass and spiculation metrics in making a finaldetermination of the suspicious locations in the digital mammogramimage.

[0089] Advantageously, in a CAD system according to a preferredembodiment, the steps 704-708 of the mass detection algorithm of FIG. 7are highly similar to the steps 304-308 of the spiculation detectionalgorithm of FIG. 3, with the exception that GMAG(i,j) is used insteadof LMAG(i,j) or W_(σ)(i,j), and with the exception that GARG(i,j) isused instead of LARG(i,j). Thus, according to a preferred embodiment, agradient plane is computed from the digital mammogram and information inthis gradient plane is processed for identifying masses in the digitalmammogram. Further, the processing of information in the gradient planecomprises the step of applying a portion of a spiculation detectionalgorithm to the gradient plane. In this manner a computer program whichhas already been written may, with minor modifications (see, e.g.,equation (13) in contrast to equation (6)), be ported into massdetection algorithms.

[0090] By way of example and not by way of limitation, typical valuesfor r_(min), r_(max), R_(icand), and K may be 4 mm, 16 mm, 2 mm, and 16,respectively. As discussed supra, it has been found that the use ofsmaller scales (smaller values of σ, such as σ=0.2 mm) during the step702 of computing the gradient magnitude GMAG(i,j) and gradient directionGARG(i,j) are better for detection of smaller masses. Larger scales(larger values of σ, such as σ=3 mm) have been found to be better fordetection of larger masses. Additionally, it has been found that smallervalues of R_(icand) during the step 706 are better for detection ofsmaller masses, while larger values are better for detection of largermasses. For example, the value of R_(icand)=2 mm may be useful fordetection of smaller masses, whereas R_(icand)=4 mm may be useful fordetection of larger masses.

[0091] Accordingly, it has been found to be advantageous to use amultiscale approach for the detection of suspicious masses in digitalmammograms. In this approach, the density magnitude metric G1(i,j) anddensity isotropy metric G2(i,j) are computed more than once usingdifferent parameter values for σ and R_(lcand), and the results aretransmitted along with other information to a subsequent linearclassifier/neural network step for an overall determination ofsuspiciousness.

[0092]FIG. 10 shows steps carried out by a CAD system in accordance withanother preferred embodiment, in which a multiscale approach for thedetection of suspicious masses is used. After a step 1002 (similar tothe step 202 of FIG. 2) is executed, a step 1004 is carried out in whichthe parameters R_(icand) and σ are set to R1 and σ1, respectively. Thedensity magnitude metric G1(i,j) and density isotropy metric G2(i,j) arethen computed in a manner similar to steps 702-708 of FIG. 7, thesemetrics being identified by the simpler notation [G1,G2]_(R1,σ1).

[0093] Following this step, at step 1006 values for σ and R_(icand) arereassigned to the values R2 and σ2, respectively, and the metrics[G1,G2]_(R2,σ2) are computed using steps similar to steps 702-708 ofFIG. 7. Following this step, at step 1008 the spiculation magnitudemetrics F1(i,j) and F2(i,j) are computed using steps similar to steps302-308 of FIG. 3, these metrics being identified by the simplernotation [F1,F2].

[0094] At step 1010 the features [G1,G2]_(R1,σ1), [G1,G2]_(R2,σ2), and[F1,F2] are processed by linear classifier and/or neural network methodsin determining suspicious masses in the digital mammogram. Accordingly,both smaller and larger masses are more reliably identified because ofthe different values of the pairs R1,σ1 and R2,σ2 used in the G1 and G2calculations.

[0095] By way of example and not by way of limitation, the values of R1and σ1 as used in step 1004 may be R1=2 mm and σ1=0.2 mm for sensitivityto smaller masses. The values of R2 and σ2, in turn, may be R2=4 mm andσ2=3 mm. Other values may be used in accordance with the preferredembodiment for optimization based on a variety of factors such as systemhardware, statistical patient characteristics, and other factors.

[0096] Finally, at step 1012, suspicious portions of the digitalmammogram are identified to the user by means of a display device.Advantageously, specificity and sensitivity are increased in the methodof FIG. 10 by the use of the “spiculatedness” or “stellateness” measuresF1 and F2 in conjunction with the mass feature metrics G1 and G2computed at different scales.

[0097] In another preferred embodiment, a variation of the steps 1004and 1006 of FIG. 10 may be used, wherein the value of r_(max) is variedinstead of R_(icand). Indeed, it has been found that a mass detectionalgorithm according to a preferred embodiment is more sensitive tovariations in r_(max) than to variations in R_(icand) for purposes ofsensitivity to different sized masses. For larger values of r_(max),pixels at the edges of larger masses are more likely to be “captured”within the annulus of FIG. 9, and thus are more likely to count towardthe values G1 _(icand) and G2 _(icand), than when smaller values ofr_(max) are used. However, larger values of r_(max) cause reducedsensitivity to smaller masses, because an unnecessarily large number ofnon-edge pixels surrounding smaller masses are “captured” within theannulus of FIG. 9. This results in higher “noise” values in theneighborhood around the center pixel, causing reduced sensitivity tosmaller masses.

[0098] Accordingly, it is advantageous to first compute the densitymagnitude metric G1(i,j) and density isotropy metric G2(i,j) for a firstpair of parameter values r_(max1) and σ1, and then to compute G1(i,j)and G2(i,j) for a second pair of parameter values r_(max2) and σ2. Thefeatures [G1,G2]_(rmax1,σ1), [G1,G2]_(rmax2,σ2), and [F1,F2] are thenprocessed by a linear classifier and/or neural network methods indetermining suspicious masses in the digital mammogram.

[0099] By way of example and not by way of limitation, typical valuesfor r_(max1), and σ1 may be 10 mm and 0.2 mm, respectively. Typicalvalues for r_(max2) and σ2 may be 16 mm and 3 mm, respectively.Importantly, as with other parameters such as r_(min) and K noted above,more optimal values for r_(max) and σ may be determined by a personskilled in the art for greater sensitivity and specificity, depending ona variety of factors such as system hardware, statistical patientcharacteristics, and other factors.

[0100] While the adaptation of a portion of a spiculation detectionengine for use in a mass detection algorithm has been disclosed in termsof the Karssemeijer metrics F1→G1 and F2→G2, other spiculation detectionalgorithms are easily adaptable for use in mass detection algorithms inaccordance with a preferred embodiment. As an example, in U.S. patentapplication Ser. No. 08/676,660, assigned to the assignee of the presentinvention, a spiculation detection algorithm for generating a cumulativearray C(i,j) was adapted for generating a mass detection measureSphericity(i,j), which is related to presence of circumscribed massescentered at (i,j). As shown in that disclosure, the described forwardtransform method applied to the line image for detecting spiculationswas advantageously adapted to be applied to the gradient image fordetecting masses.

[0101] While preferred embodiments of the invention have been described,these descriptions are merely illustrative and are not intended to limitthe present invention. For example, although the embodiments of theinvention described above were in the context of a system for computeraided diagnosis and detection of breast carcinoma in x-ray films, thoseskilled in the art will recognize that the disclosed methods andstructures are readily adaptable for broader applications. For example,the invention is applicable to many other types of CAD systems fordetection of other types of medical abnormalities. Thus, the specificembodiments described here and above are given by way of example onlyand the invention is limited only by the terms of the appended claims.

What is claimed is:
 1. A method of detecting masses in a digitalmammogram, comprising the steps of: computing a gradient plane from saiddigital mammogram; processing information in said gradient plane foridentifying masses in said digital mammogram.
 2. The method of claim 1,wherein said step of processing information in said gradient planecomprises the step of applying a portion of a spiculation detectionalgorithm to said gradient plane.
 3. The method of claim 2, saidspiculation detection algorithm comprising: a line detection step forgenerating line information and direction information corresponding tothe digital mammogram; and a post-line detection step for identifyingspiculations in the digital mammogram using said line information andsaid direction information; wherein the portion of said spiculationalgorithm which is applied to said gradient plane is said post-linedetection step.
 4. The method of claim 3, said gradient plane comprisinggradient magnitude information and gradient direction information,wherein: when said post-line detection step of said spiculationdetection algorithm is used in said spiculation detection algorithm,said post-line detection step receives a first input equal to said lineinformation from said line detection step of said spiculation detectionalgorithm, said post-line detection step of the spiculation detectionalgorithm also receiving a second input, said second input being equalto said direction information from said line detection step of saidspiculation detection algorithm; and wherein when said post-linedetection step of said spiculation detection algorithm is applied tosaid gradient plane, said gradient magnitude information is received assaid first input and said gradient direction information is received assaid second input.
 5. The method of claim 4, wherein: when saidpost-line detection step of said spiculation detection algorithm is usedin said spiculation detection algorithm, said post-line detection stepgenerates a first output corresponding to spiculation locationinformation; and wherein when said post-line detection step of saidspiculation detection algorithm is applied to said gradient plane, saidfirst output corresponds to mass location information.
 6. The method ofclaim 5, wherein: when said post-line detection step of said spiculationdetection algorithm is used in said spiculation detection algorithm,said post-line detection step generates a second output corresponding tospiculation intensity information; and wherein when said post-linedetection step of said spiculation detection algorithm is applied tosaid gradient plane, said first output corresponds to mass intensityinformation.
 7. The method of claim 6, said digital mammogram comprisingpixels, said line information and said direction information formed in aline image plane comprising pixels, wherein said post-line detectionstep of said spiculation detection algorithm comprises the steps of:receiving said line image plane; selecting a set of candidate pixels insaid digital mammogram; for each candidate pixel: selecting aneighborhood of pixels near said candidate pixel; selecting a smallregion around said candidate pixel; computing a first spiculation metricproportional to the number of pixels in said neighborhood which arelocated along lines in said line image plane and which have directioninformation pointing toward said small region; and evaluating said firstspiculation metrics of said candidate pixels for determining thelocations of spiculations in said digital mammogram.
 8. The method ofclaim 7, said post-line detection step of said spiculation detectionalgorithm further comprising the steps of: for each candidate pixel:computing a second spiculation metric corresponding to the spatialdistribution of those pixels in said neighborhood which are locatedalong lines in said line image plane and which have directioninformation pointing toward said small region, said second spiculationmetric increasing according to the isotropy of said spatial distributionaround said candidate pixel; and evaluating said first and secondspiculation metrics of said candidate pixels for determining thelocations of spiculations in said digital mammogram.
 9. The method ofclaim 8, wherein said set of candidate pixels comprises each pixel insaid line image.
 10. A method of detecting masses in a digitalmammogram, comprising the steps of: computing a gradient plane from saiddigital mammogram, said gradient plane comprising pixels, each pixelhaving gradient magnitude and gradient direction information; selectinga set of candidate pixels in digital mammogram image; for each candidatepixel, computing a first density metric based on a first set ofsurrounding pixels having gradient magnitudes above a first thresholdand having gradient directions pointing generally toward said candidatepixel; and evaluating said first density metrics for determining thelocations of masses in said digital mammogram.
 11. The method of claim10, further comprising the steps of: for each candidate pixel, computinga second density metric corresponding to a spatial distribution of saidfirst set of pixels, said second density metric corresponding to theisotropy of said spatial distribution around said candidate pixel; andevaluating said first and second density metrics of said candidatepixels for determining the locations of masses in said digitalmammogram.
 12. The method of claim 11, said candidate pixels beingidentified according to an index icand, said first density metric forthe icand^(th) candidate pixel being denoted G1 _(icand), said firstdensity metric G1 _(icand) being computed according to the steps of:selecting a neighborhood of pixels NH_(icand) around said candidatepixel; selecting a small region R_(icand) around said candidate pixel;selecting said first set of pixels from a set of pixels lying in saidneighborhood NH_(icand) having directions which point toward said smallregion R_(icand); and counting the number of pixels in said first set;wherein said first density metric G1 _(icand) is proportional to thenumber of pixels in said first subset.
 13. The method of claim 12, saidfirst set of pixels corresponding to the icand^(th) candidate pixelbeing denoted by an index (icand,jpoint), said second density metric forthe icand^(th) candidate pixel being denoted G2 _(icand), said seconddensity metric G2 _(icand) being computed according to the steps of:selecting K spatial bins (icand,k) extending radially from saidcandidate pixel and being arranged in a radially symmetric manner aroundsaid candidate pixel; for each pixel (icand,jpoint) of said first set ofpixels, identifying the spatial bin (icand,k) in which said pixel(icand,jpoint) is located; and computing a number of pixels n_(icand,k)in each spatial bin (icand,k); wherein said second density metric G2_(icand) is based on the statistical distribution of the numbern_(icand,k) as k is varied.
 14. The method of claim 13, wherein G2_(icand) is proportional to the number of values k for which n_(icand,k)is greater than a median value calculated for random orientations. 15.The method of claim 11, wherein said step of evaluating said first andsecond density metrics is performed according to a linear classifiermethod.
 16. The method of claim 11, wherein said step of evaluating saidfirst and second density metrics is performed according to a neuralnetwork method.
 17. The method of claim 10, wherein said set ofcandidate pixels comprises each pixel in said gradient plane.
 18. Themethod of claim 12, wherein said neighborhood of pixels NH_(icand) formsan annular region around said icand^(th) candidate pixel.
 19. The methodof claim 18, wherein said small region R_(icand) is a circular regionlying within said annular region formed by said neighborhood of pixelsneighborhood of pixels NH_(icand).
 20. A method of detecting masses in adigital mammogram, comprising the steps of: computing a gradient planefrom said digital mammogram, said gradient plane comprising pixels, eachpixel having gradient magnitude and gradient direction information;selecting a set of candidate pixels in said gradient plane, saidcandidate pixels being denoted by an index icand; for each candidatepixel icand, computing a first density metric G1 _(icand) according tothe steps of: selecting a neighborhood of pixels NH_(icand) around saidcandidate pixel; selecting a small region R_(icand) around saidcandidate pixel; selecting a first set of pixels in said neighborhoodNH_(icand) having gradient directions pointing toward said small regionR_(icand) and having a gradient magnitude greater than a predeterminedlower threshold, said first set of pixels being denoted by the countervariable jpoint; and counting the number of pixels in said first set,wherein said first density metric G1 _(icand) is proportional to thenumber of pixels in said first set; for each candidate pixel icand,computing a second density metric G2 _(icand) according to the steps of:selecting K spatial bins (icand,k) extending radially from saidcandidate pixel and being arranged in a radially symmetric manner aroundsaid candidate pixel; for each pixel (icand,jpoint) of said first set ofpixels, identifying the spatial bin (icand,k) in which said pixel(icand,jpoint) is located; and computing a number of pixels n_(icand,k)in each spatial bin (icand,k), wherein said second density metric G2_(icand) is based on the statistical distribution of the numbern_(icand,k) as k is varied; and evaluating said first and second densitymetrics G1 _(icand) and G2 _(icand) according to a linear classifiermethod for determining the locations of masses in said digitalmammogram.